optirs_core/privacy/federated_privacy/

components.rs

// Component implementations for federated privacy algorithms

use super::super::PrivacyBudget;
use super::config::*;
use crate::error::Result;
use scirs2_core::ndarray::Array1;
use scirs2_core::numeric::Float;
use scirs2_core::random::Random;
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;
use std::sync::Arc;

// Advanced federated learning implementation structures

/// Byzantine-robust aggregation engine
pub struct ByzantineRobustAggregator<T: Float + Debug + Send + Sync + 'static> {
    config: ByzantineRobustConfig,
    client_reputations: HashMap<String, f64>,
    outlier_history: VecDeque<OutlierDetectionResult>,
    statistical_analyzer: StatisticalAnalyzer<T>,
    robust_estimators: RobustEstimators<T>,
}

/// Personalized federated learning manager
pub struct PersonalizationManager<T: Float + Debug + Send + Sync + 'static> {
    config: PersonalizationConfig,
    client_models: HashMap<String, PersonalizedModel<T>>,
    global_model: Option<Array1<T>>,
    clustering_engine: ClusteringEngine<T>,
    meta_learner: FederatedMetaLearner<T>,
    adaptation_tracker: AdaptationTracker<T>,
}

/// Adaptive privacy budget manager
pub struct AdaptiveBudgetManager<T: Float + Debug + Send + Sync + 'static> {
    config: AdaptiveBudgetConfig,
    client_budgets: HashMap<String, AdaptiveBudget>,
    global_budget_tracker: GlobalBudgetTracker,
    utility_estimator: UtilityEstimator,
    fairness_monitor: FairnessMonitor,
    contextual_analyzer: ContextualAnalyzer,
    _phantom: std::marker::PhantomData<T>,
}

/// Communication efficiency optimizer
pub struct CommunicationOptimizer<T: Float + Debug + Send + Sync + 'static> {
    config: CommunicationConfig,
    compression_engine: CompressionEngine<T>,
    bandwidth_monitor: BandwidthMonitor,
    transmission_scheduler: TransmissionScheduler,
    gradient_buffers: HashMap<String, GradientBuffer<T>>,
    quality_controller: QualityController,
}

/// Continual learning coordinator
pub struct ContinualLearningCoordinator<T: Float + Debug + Send + Sync + 'static> {
    config: ContinualLearningConfig,
    task_detector: TaskDetector<T>,
    memory_manager: MemoryManager<T>,
    knowledge_transfer_engine: KnowledgeTransferEngine<T>,
    forgetting_prevention: ForgettingPreventionEngine<T>,
    task_history: VecDeque<TaskInfo>,
}

// Supporting implementation structures

/// Statistical analyzer for Byzantine detection
pub struct StatisticalAnalyzer<T: Float + Debug + Send + Sync + 'static> {
    window_size: usize,
    significancelevel: f64,
    test_statistics: VecDeque<TestStatistic<T>>,
}

/// Robust estimators for aggregation
pub struct RobustEstimators<T: Float + Debug + Send + Sync + 'static> {
    trimmed_mean_cache: HashMap<String, T>,
    median_cache: HashMap<String, T>,
    krum_scores: HashMap<String, f64>,
}

/// Outlier detection result
#[derive(Debug, Clone)]
pub struct OutlierDetectionResult {
    pub clientid: String,
    pub round: usize,
    pub is_outlier: bool,
    pub outlier_score: f64,
    pub detection_method: String,
}

/// Personalized model for each client
#[derive(Debug, Clone)]
pub struct PersonalizedModel<T: Float + Debug + Send + Sync + 'static> {
    pub model_parameters: Array1<T>,
    pub personal_layers: HashMap<usize, Array1<T>>,
    pub adaptation_state: AdaptationState<T>,
    pub performance_history: Vec<f64>,
    pub last_update_round: usize,
}

/// Adaptation state for personalized models
#[derive(Debug, Clone)]
pub struct AdaptationState<T: Float + Debug + Send + Sync + 'static> {
    pub learning_rate: f64,
    pub momentum: Array1<T>,
    pub adaptation_count: usize,
    pub gradient_history: VecDeque<Array1<T>>,
}

/// Clustering engine for federated learning
pub struct ClusteringEngine<T: Float + Debug + Send + Sync + 'static> {
    method: ClusteringMethod,
    cluster_centers: HashMap<usize, Array1<T>>,
    client_clusters: HashMap<String, usize>,
    cluster_update_counter: usize,
}

/// Federated meta-learner
pub struct FederatedMetaLearner<T: Float + Debug + Send + Sync + 'static> {
    meta_parameters: Array1<T>,
    client_adaptations: HashMap<String, Array1<T>>,
    meta_gradient_buffer: Array1<T>,
    task_distributions: HashMap<String, TaskDistribution<T>>,
}

/// Task distribution for meta-learning
#[derive(Debug, Clone)]
pub struct TaskDistribution<T: Float + Debug + Send + Sync + 'static> {
    pub support_gradient: Array1<T>,
    pub query_gradient: Array1<T>,
    pub task_similarity: f64,
    pub adaptation_steps: usize,
}

/// Adaptation tracker
pub struct AdaptationTracker<T: Float + Debug + Send + Sync + 'static> {
    adaptation_history: HashMap<String, Vec<AdaptationEvent<T>>>,
    convergence_metrics: HashMap<String, ConvergenceMetrics>,
}

/// Adaptation event
#[derive(Debug, Clone)]
pub struct AdaptationEvent<T: Float + Debug + Send + Sync + 'static> {
    pub round: usize,
    pub parameter_change: Array1<T>,
    pub loss_improvement: f64,
    pub adaptation_method: String,
}

/// Convergence metrics
#[derive(Debug, Clone)]
pub struct ConvergenceMetrics {
    pub convergence_rate: f64,
    pub stability_measure: f64,
    pub adaptation_efficiency: f64,
}

/// Adaptive budget for each client
#[derive(Debug, Clone)]
pub struct AdaptiveBudget {
    pub current_epsilon: f64,
    pub current_delta: f64,
    pub allocated_epsilon: f64,
    pub allocated_delta: f64,
    pub consumption_rate: f64,
    pub importance_weight: f64,
    pub context_factors: HashMap<String, f64>,
}

/// Global budget tracker
pub struct GlobalBudgetTracker {
    total_allocated: f64,
    consumption_history: VecDeque<BudgetConsumption>,
    allocation_strategy: BudgetAllocationStrategy,
}

/// Budget consumption record
#[derive(Debug, Clone)]
pub struct BudgetConsumption {
    pub round: usize,
    pub clientid: String,
    pub epsilonconsumed: f64,
    pub delta_consumed: f64,
    pub utility_achieved: f64,
}

/// Utility estimator
pub struct UtilityEstimator {
    utility_history: VecDeque<UtilityMeasurement>,
    prediction_model: UtilityPredictionModel,
}

/// Utility measurement
#[derive(Debug, Clone)]
pub struct UtilityMeasurement {
    pub round: usize,
    pub accuracy: f64,
    pub loss: f64,
    pub convergence_rate: f64,
    pub noise_level: f64,
}

/// Utility prediction model
pub struct UtilityPredictionModel {
    model_type: String,
    parameters: HashMap<String, f64>,
}

/// Fairness monitor
pub struct FairnessMonitor {
    fairness_metrics: FairnessMetrics,
    client_fairness_scores: HashMap<String, f64>,
    fairness_constraints: Vec<FairnessConstraint>,
}

/// Fairness metrics
#[derive(Debug, Clone)]
pub struct FairnessMetrics {
    pub demographic_parity: f64,
    pub equalized_opportunity: f64,
    pub individual_fairness: f64,
    pub group_fairness: f64,
}

/// Fairness constraint
#[derive(Debug, Clone)]
pub struct FairnessConstraint {
    pub constraint_type: String,
    pub threshold: f64,
    pub affected_groups: Vec<String>,
}

/// Selection diversity metrics for client sampling
#[derive(Debug, Clone)]
pub struct SelectionDiversityMetrics {
    pub geographic_diversity: f64,
    pub demographic_diversity: f64,
    pub resource_diversity: f64,
    pub temporal_diversity: f64,
}

/// Contextual analyzer
pub struct ContextualAnalyzer {
    context_history: VecDeque<ContextSnapshot>,
    context_model: ContextModel,
}

/// Context snapshot
#[derive(Debug, Clone)]
pub struct ContextSnapshot {
    pub timestamp: u64,
    pub context_factors: HashMap<String, f64>,
    pub privacy_requirement: f64,
    pub utility_requirement: f64,
}

/// Context model for privacy adaptation
pub struct ContextModel {
    model_parameters: HashMap<String, f64>,
    adaptation_learning_rate: f64,
}

/// Compression engine
pub struct CompressionEngine<T: Float + Debug + Send + Sync + 'static> {
    strategy: CompressionStrategy,
    compression_history: VecDeque<CompressionResult<T>>,
    error_feedback_memory: HashMap<String, Array1<T>>,
}

/// Compression result
#[derive(Debug, Clone)]
pub struct CompressionResult<T: Float + Debug + Send + Sync + 'static> {
    pub original_size: usize,
    pub compressed_size: usize,
    pub compressionratio: f64,
    pub reconstruction_error: T,
    pub compression_time: u64,
}

/// Bandwidth monitor
pub struct BandwidthMonitor {
    bandwidth_history: VecDeque<BandwidthMeasurement>,
    current_conditions: NetworkConditions,
}

/// Bandwidth measurement
#[derive(Debug, Clone)]
pub struct BandwidthMeasurement {
    pub timestamp: u64,
    pub upload_bandwidth: f64,
    pub download_bandwidth: f64,
    pub latency: f64,
    pub packet_loss: f64,
}

/// Network conditions
#[derive(Debug, Clone)]
pub struct NetworkConditions {
    pub available_bandwidth: f64,
    pub network_quality: NetworkQuality,
    pub congestion_level: f64,
}

#[derive(Debug, Clone, Copy)]
pub enum NetworkQuality {
    Excellent,
    Good,
    Fair,
    Poor,
}

/// Transmission scheduler
pub struct TransmissionScheduler {
    schedule_queue: VecDeque<TransmissionTask>,
    priority_weights: HashMap<String, f64>,
}

/// Transmission task
#[derive(Debug, Clone)]
pub struct TransmissionTask {
    pub clientid: String,
    pub data_size: usize,
    pub priority: f64,
    pub deadline: u64,
    pub compression_required: bool,
}

/// Gradient buffer for communication optimization
pub struct GradientBuffer<T: Float + Debug + Send + Sync + 'static> {
    buffered_gradients: VecDeque<Array1<T>>,
    staleness_tolerance: usize,
    buffer_capacity: usize,
}

/// Quality controller for communication
pub struct QualityController {
    qos_requirements: QoSConfig,
    performance_monitor: PerformanceMonitor,
}

/// Performance monitor
pub struct PerformanceMonitor {
    latency_measurements: VecDeque<f64>,
    throughput_measurements: VecDeque<f64>,
    quality_violations: usize,
}

/// Task detector for continual learning
pub struct TaskDetector<T: Float + Debug + Send + Sync + 'static> {
    detection_method: TaskDetectionMethod,
    gradient_buffer: VecDeque<Array1<T>>,
    change_points: Vec<ChangePoint>,
    detection_threshold: f64,
}

/// Change point for task detection
#[derive(Debug, Clone)]
pub struct ChangePoint {
    pub round: usize,
    pub confidence: f64,
    pub change_magnitude: f64,
}

/// Memory manager for continual learning
pub struct MemoryManager<T: Float + Debug + Send + Sync + 'static> {
    memory_budget: usize,
    stored_examples: VecDeque<MemoryExample<T>>,
    eviction_strategy: EvictionStrategy,
    compression_enabled: bool,
}

/// Memory example for continual learning
#[derive(Debug, Clone)]
pub struct MemoryExample<T: Float + Debug + Send + Sync + 'static> {
    pub features: Array1<T>,
    pub target: Array1<T>,
    pub importance: f64,
    pub timestamp: u64,
    pub task_id: usize,
}

/// Knowledge transfer engine
pub struct KnowledgeTransferEngine<T: Float + Debug + Send + Sync + 'static> {
    transfer_method: KnowledgeTransferMethod,
    transfer_matrices: HashMap<String, Array1<T>>,
    similarity_cache: HashMap<String, f64>,
}

/// Forgetting prevention engine
pub struct ForgettingPreventionEngine<T: Float + Debug + Send + Sync + 'static> {
    method: ForgettingPreventionMethod,
    importance_weights: HashMap<String, Array1<T>>,
    regularization_strength: f64,
    memory_replay_buffer: VecDeque<Array1<T>>,
}

/// Task information
#[derive(Debug, Clone)]
pub struct TaskInfo {
    pub task_id: usize,
    pub start_round: usize,
    pub end_round: Option<usize>,
    pub task_description: String,
    pub performance_metrics: HashMap<String, f64>,
}

/// Test statistic for outlier detection
#[derive(Debug, Clone)]
pub struct TestStatistic<T: Float + Debug + Send + Sync + 'static> {
    pub round: usize,
    pub statistic_value: T,
    pub p_value: f64,
    pub test_type: StatisticalTestType,
    pub clientid: String,
}

/// Secure aggregation protocol implementation
pub struct SecureAggregator<T: Float + Debug + Send + Sync + 'static> {
    config: SecureAggregationConfig,
    client_masks: HashMap<String, Array1<T>>,
    shared_randomness: Arc<std::sync::Mutex<u64>>,
    aggregation_threshold: usize,
    round_keys: Vec<u64>,
}

/// Privacy amplification analyzer
pub struct PrivacyAmplificationAnalyzer {
    config: AmplificationConfig,
    subsampling_history: VecDeque<SubsamplingEvent>,
    amplification_factors: HashMap<String, f64>,
}

/// Cross-device privacy manager
pub struct CrossDevicePrivacyManager<T: Float + Debug + Send + Sync + 'static> {
    config: CrossDeviceConfig,
    user_clusters: HashMap<String, Vec<String>>,
    device_profiles: HashMap<String, DeviceProfile<T>>,
    temporal_correlations: HashMap<String, Vec<TemporalEvent>>,
}

/// Federated composition analyzer
pub struct FederatedCompositionAnalyzer {
    method: FederatedCompositionMethod,
    round_compositions: Vec<RoundComposition>,
    client_compositions: HashMap<String, Vec<ClientComposition>>,
}

/// Client participation in a round
#[derive(Debug, Clone)]
pub struct ParticipationRound {
    pub round: usize,
    pub participating_clients: Vec<String>,
    pub sampling_probability: f64,
    pub privacy_cost: PrivacyCost,
    pub aggregation_noise: f64,
}

/// Privacy cost breakdown
#[derive(Debug, Clone)]
pub struct PrivacyCost {
    pub epsilon: f64,
    pub delta: f64,
    pub client_contribution: f64,
    pub amplificationfactor: f64,
    pub composition_cost: f64,
}

/// Subsampling event for amplification analysis
#[derive(Debug, Clone)]
pub struct SubsamplingEvent {
    pub round: usize,
    pub sampling_rate: f64,
    pub clients_sampled: usize,
    pub total_clients: usize,
    pub amplificationfactor: f64,
}

/// Device profile for cross-device privacy
#[derive(Debug, Clone)]
pub struct DeviceProfile<T: Float + Debug + Send + Sync + 'static> {
    pub device_id: String,
    pub user_id: String,
    pub device_type: DeviceType,
    pub location_cluster: String,
    pub participation_frequency: f64,
    pub local_privacy_budget: PrivacyBudget,
    pub sensitivity_estimate: T,
}

/// Device types for privacy analysis
#[derive(Debug, Clone, Copy, Eq, Hash, PartialEq)]
pub enum DeviceType {
    Mobile,
    Desktop,
    IoT,
    Edge,
    Server,
}

/// Temporal event for privacy tracking
#[derive(Debug, Clone)]
pub struct TemporalEvent {
    pub timestamp: u64,
    pub event_type: TemporalEventType,
    pub privacy_impact: f64,
}

#[derive(Debug, Clone)]
pub enum TemporalEventType {
    ClientParticipation,
    ModelUpdate,
    PrivacyBudgetConsumption,
    AggregationEvent,
}

/// Round composition for privacy accounting
#[derive(Debug, Clone)]
pub struct RoundComposition {
    pub round: usize,
    pub participating_clients: usize,
    pub epsilonconsumed: f64,
    pub delta_consumed: f64,
    pub amplification_applied: bool,
    pub composition_method: FederatedCompositionMethod,
}

/// Client-specific composition tracking
#[derive(Debug, Clone)]
pub struct ClientComposition {
    pub clientid: String,
    pub round: usize,
    pub local_epsilon: f64,
    pub local_delta: f64,
    pub contribution_weight: f64,
}

// Implementation blocks for components

impl ContextualAnalyzer {
    /// Create a new contextual analyzer
    pub fn new() -> Self {
        Self {
            context_history: VecDeque::with_capacity(100),
            context_model: ContextModel::new(),
        }
    }
}

impl Default for ContextualAnalyzer {
    fn default() -> Self {
        Self::new()
    }
}

impl ContextModel {
    /// Create a new context model
    pub fn new() -> Self {
        Self {
            model_parameters: HashMap::new(),
            adaptation_learning_rate: 0.01,
        }
    }
}

impl Default for ContextModel {
    fn default() -> Self {
        Self::new()
    }
}

impl FairnessMonitor {
    /// Create a new fairness monitor
    pub fn new() -> Self {
        Self {
            fairness_metrics: FairnessMetrics {
                demographic_parity: 0.0,
                equalized_opportunity: 0.0,
                individual_fairness: 0.0,
                group_fairness: 0.0,
            },
            client_fairness_scores: HashMap::new(),
            fairness_constraints: Vec::new(),
        }
    }

    /// Get current fairness metrics
    pub fn get_metrics(&self) -> &FairnessMetrics {
        &self.fairness_metrics
    }

    /// Compute fairness weights for clients
    pub fn compute_fairness_weights(&self, clientids: &[String]) -> HashMap<String, f64> {
        let mut weights = HashMap::new();
        for clientid in clientids {
            // Use existing fairness score or default to 1.0
            let weight = self
                .client_fairness_scores
                .get(clientid)
                .copied()
                .unwrap_or(1.0);
            weights.insert(clientid.clone(), weight);
        }
        weights
    }
}

impl Default for FairnessMonitor {
    fn default() -> Self {
        Self::new()
    }
}

impl<
        T: Float
            + Debug
            + Send
            + Sync
            + 'static
            + Default
            + Clone
            + scirs2_core::ndarray::ScalarOperand,
    > FederatedMetaLearner<T>
{
    /// Create a new federated meta-learner
    pub fn new(_parametersize: usize) -> Self {
        Self {
            meta_parameters: Array1::default(0),
            client_adaptations: HashMap::new(),
            meta_gradient_buffer: Array1::default(0),
            task_distributions: HashMap::new(),
        }
    }

    /// Compute meta-gradients for adaptation from client gradients
    pub fn compute_client_meta_gradients(
        &mut self,
        _client_gradients: &HashMap<String, Array1<T>>,
        _support_data: &HashMap<String, Array1<T>>,
        _query_data: &HashMap<String, Array1<T>>,
    ) -> Result<Array1<T>> {
        // Placeholder implementation
        Ok(Array1::default(0))
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> ClusteringEngine<T> {
    /// Create a new clustering engine
    pub fn new() -> Self {
        Self {
            method: ClusteringMethod::KMeans,
            cluster_centers: HashMap::new(),
            client_clusters: HashMap::new(),
            cluster_update_counter: 0,
        }
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> Default for ClusteringEngine<T> {
    fn default() -> Self {
        Self::new()
    }
}

impl<T: Float + Debug + Send + Sync + 'static> AdaptationTracker<T> {
    /// Create a new adaptation tracker
    pub fn new() -> Self {
        Self {
            adaptation_history: HashMap::new(),
            convergence_metrics: HashMap::new(),
        }
    }
}

impl<T: Float + Debug + Send + Sync + 'static> Default for AdaptationTracker<T> {
    fn default() -> Self {
        Self::new()
    }
}

impl GlobalBudgetTracker {
    /// Create a new global budget tracker
    pub fn new() -> Self {
        Self {
            total_allocated: 0.0,
            consumption_history: VecDeque::with_capacity(1000),
            allocation_strategy: BudgetAllocationStrategy::Uniform,
        }
    }
}

impl Default for GlobalBudgetTracker {
    fn default() -> Self {
        Self::new()
    }
}

impl<T: Float + Debug + Send + Sync + 'static> TaskDetector<T> {
    /// Create a new task detector
    pub fn new() -> Self {
        Self {
            detection_method: TaskDetectionMethod::GradientBased,
            gradient_buffer: VecDeque::with_capacity(100),
            change_points: Vec::new(),
            detection_threshold: 0.1,
        }
    }

    /// Detect task change based on gradients
    pub fn detect_task_change(&mut self, updates: &[Array1<T>]) -> Result<bool> {
        // Placeholder implementation
        let _ = updates;
        Ok(false)
    }
}

impl<T: Float + Debug + Send + Sync + 'static> Default for TaskDetector<T> {
    fn default() -> Self {
        Self::new()
    }
}

impl TransmissionScheduler {
    /// Create a new transmission scheduler
    pub fn new() -> Self {
        Self {
            schedule_queue: VecDeque::new(),
            priority_weights: HashMap::new(),
        }
    }
}

impl Default for TransmissionScheduler {
    fn default() -> Self {
        Self::new()
    }
}

impl QualityController {
    /// Create a new quality controller
    pub fn new() -> Self {
        Self {
            qos_requirements: QoSConfig::default(),
            performance_monitor: PerformanceMonitor::new(),
        }
    }
}

impl Default for QualityController {
    fn default() -> Self {
        Self::new()
    }
}

impl PerformanceMonitor {
    /// Create a new performance monitor
    pub fn new() -> Self {
        Self {
            latency_measurements: VecDeque::with_capacity(1000),
            throughput_measurements: VecDeque::with_capacity(1000),
            quality_violations: 0,
        }
    }
}

impl Default for PerformanceMonitor {
    fn default() -> Self {
        Self::new()
    }
}

impl<T: Float + Debug + Send + Sync + 'static> MemoryManager<T> {
    /// Create a new memory manager
    pub fn new() -> Self {
        Self {
            memory_budget: 1000,
            stored_examples: VecDeque::new(),
            eviction_strategy: EvictionStrategy::LRU,
            compression_enabled: false,
        }
    }
}

impl<T: Float + Debug + Send + Sync + 'static> Default for MemoryManager<T> {
    fn default() -> Self {
        Self::new()
    }
}

impl<T: Float + Debug + Send + Sync + 'static> KnowledgeTransferEngine<T> {
    /// Create a new knowledge transfer engine
    pub fn new() -> Self {
        Self {
            transfer_method: KnowledgeTransferMethod::ParameterTransfer,
            transfer_matrices: HashMap::new(),
            similarity_cache: HashMap::new(),
        }
    }
}

impl<T: Float + Debug + Send + Sync + 'static> Default for KnowledgeTransferEngine<T> {
    fn default() -> Self {
        Self::new()
    }
}

impl<T: Float + Debug + Send + Sync + 'static> ForgettingPreventionEngine<T> {
    /// Create a new forgetting prevention engine
    pub fn new() -> Self {
        Self {
            method: ForgettingPreventionMethod::EWC,
            importance_weights: HashMap::new(),
            regularization_strength: 0.1,
            memory_replay_buffer: VecDeque::new(),
        }
    }
}

impl<T: Float + Debug + Send + Sync + 'static> Default for ForgettingPreventionEngine<T> {
    fn default() -> Self {
        Self::new()
    }
}

// Default implementations for component creation

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> ByzantineRobustAggregator<T> {
    /// Create a new Byzantine robust aggregator
    pub fn new() -> Result<Self> {
        Ok(Self {
            config: ByzantineRobustConfig {
                method: ByzantineRobustMethod::TrimmedMean { trim_ratio: 0.2 },
                expected_byzantine_ratio: 0.2,
                dynamic_detection: true,
                reputation_system: ReputationSystemConfig::default(),
                statistical_tests: StatisticalTestConfig::default(),
            },
            client_reputations: HashMap::new(),
            outlier_history: VecDeque::new(),
            statistical_analyzer: StatisticalAnalyzer {
                window_size: 10,
                significancelevel: 0.05,
                test_statistics: VecDeque::new(),
            },
            robust_estimators: RobustEstimators {
                trimmed_mean_cache: HashMap::new(),
                median_cache: HashMap::new(),
                krum_scores: HashMap::new(),
            },
        })
    }
}

impl<
        T: Float
            + Debug
            + Send
            + Sync
            + 'static
            + Default
            + Clone
            + scirs2_core::ndarray::ScalarOperand,
    > PersonalizationManager<T>
{
    /// Create a new personalization manager
    pub fn new() -> Result<Self> {
        Ok(Self {
            config: PersonalizationConfig {
                strategy: PersonalizationStrategy::None,
                local_adaptation: LocalAdaptationConfig::default(),
                clustering: ClusteringConfig::default(),
                meta_learning: MetaLearningConfig::default(),
                privacy_preserving: false,
            },
            client_models: HashMap::new(),
            global_model: None,
            clustering_engine: ClusteringEngine::new(),
            meta_learner: FederatedMetaLearner::new(0),
            adaptation_tracker: AdaptationTracker::new(),
        })
    }
}

impl<T: Float + Debug + Send + Sync + 'static> AdaptiveBudgetManager<T> {
    /// Create a new adaptive budget manager
    pub fn new() -> Result<Self> {
        Ok(Self {
            config: AdaptiveBudgetConfig::default(),
            client_budgets: HashMap::new(),
            global_budget_tracker: GlobalBudgetTracker::new(),
            utility_estimator: UtilityEstimator {
                utility_history: VecDeque::new(),
                prediction_model: UtilityPredictionModel {
                    model_type: "linear".to_string(),
                    parameters: HashMap::new(),
                },
            },
            fairness_monitor: FairnessMonitor::new(),
            contextual_analyzer: ContextualAnalyzer::new(),
            _phantom: std::marker::PhantomData,
        })
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default> CommunicationOptimizer<T> {
    /// Create a new communication optimizer
    pub fn new() -> Result<Self> {
        Ok(Self {
            config: CommunicationConfig {
                compression: CompressionStrategy::None,
                lazy_aggregation: LazyAggregationConfig::default(),
                federated_dropout: FederatedDropoutConfig::default(),
                async_updates: AsyncUpdateConfig::default(),
                bandwidth_adaptation: BandwidthAdaptationConfig::default(),
            },
            compression_engine: CompressionEngine {
                strategy: CompressionStrategy::None,
                compression_history: VecDeque::new(),
                error_feedback_memory: HashMap::new(),
            },
            bandwidth_monitor: BandwidthMonitor {
                bandwidth_history: VecDeque::new(),
                current_conditions: NetworkConditions {
                    available_bandwidth: 100.0,
                    network_quality: NetworkQuality::Good,
                    congestion_level: 0.5,
                },
            },
            transmission_scheduler: TransmissionScheduler::new(),
            gradient_buffers: HashMap::new(),
            quality_controller: QualityController::new(),
        })
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default> ContinualLearningCoordinator<T> {
    /// Create a new continual learning coordinator
    pub fn new() -> Result<Self> {
        Ok(Self {
            config: ContinualLearningConfig {
                strategy: ContinualLearningStrategy::TaskAgnostic,
                memory_management: MemoryManagementConfig::default(),
                task_detection: TaskDetectionConfig::default(),
                knowledge_transfer: KnowledgeTransferConfig::default(),
                forgetting_prevention: ForgettingPreventionConfig::default(),
            },
            task_detector: TaskDetector::new(),
            memory_manager: MemoryManager::new(),
            knowledge_transfer_engine: KnowledgeTransferEngine::new(),
            forgetting_prevention: ForgettingPreventionEngine::new(),
            task_history: VecDeque::new(),
        })
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default> SecureAggregator<T> {
    /// Create a new secure aggregator
    pub fn new(config: SecureAggregationConfig) -> Result<Self> {
        Ok(Self {
            config,
            client_masks: HashMap::new(),
            shared_randomness: Arc::new(std::sync::Mutex::new(0u64)),
            aggregation_threshold: 10,
            round_keys: Vec::new(),
        })
    }
}

impl PrivacyAmplificationAnalyzer {
    /// Create a new privacy amplification analyzer
    pub fn new(config: AmplificationConfig) -> Self {
        Self {
            config,
            subsampling_history: VecDeque::new(),
            amplification_factors: HashMap::new(),
        }
    }

    /// Compute amplification factor for current round
    pub fn compute_amplification_factor(
        &mut self,
        sampling_rate: f64,
        round: usize,
    ) -> Result<f64> {
        // Placeholder implementation
        let amplification_factor = if self.config.enabled {
            (1.0 / sampling_rate).sqrt()
        } else {
            1.0
        };

        self.subsampling_history.push_back(SubsamplingEvent {
            round,
            sampling_rate,
            clients_sampled: (sampling_rate * 1000.0) as usize,
            total_clients: 1000,
            amplificationfactor: amplification_factor,
        });

        Ok(amplification_factor)
    }
}

impl<T: Float + Debug + Send + Sync + 'static> CrossDevicePrivacyManager<T> {
    /// Create a new cross-device privacy manager
    pub fn new(config: CrossDeviceConfig) -> Self {
        Self {
            config,
            user_clusters: HashMap::new(),
            device_profiles: HashMap::new(),
            temporal_correlations: HashMap::new(),
        }
    }
}

impl FederatedCompositionAnalyzer {
    /// Create a new federated composition analyzer
    pub fn new(method: FederatedCompositionMethod) -> Self {
        Self {
            method,
            round_compositions: Vec::new(),
            client_compositions: HashMap::new(),
        }
    }
}